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Glossary#

Cartopy#

A python package for producing maps, and other geospatial data. Allows plotting on these maps, over a range of projections.

Related: Matplotlib
More information: CartoPy Site

CF Conventions#

Rules for storing meteorological Climate and Forecast data in NetCDF Format files, defining a standard metadata format to describe what the data is. This also forms the data model which iris is based on.

Related: NetCDF Format
More information: CF Conventions

Coordinate#

A container for data points, comes in three main flavours.

  • Dimensional Coordinate -

    A coordinate that describes a single data dimension of a cube. They can only contain numerical values, in a sorted order (ascending or descending).

  • Auxiliary Coordinate -

    A coordinate that can map to multiple data dimensions. Can contain any type of data.

  • Scalar Coordinate -

    A coordinate that is not mapped to any data dimension, instead representing the cube as a whole.

Related: Cube
More information: Iris Data Structures

Cube#

Cubes are the main method of storing data in Iris. A cube can consist of:

Related: NumPy
More information: Iris Data Structures

Cell Method#

A cell method represents that a cube’s data has been derived from a past statistical operation, such as a MEAN or SUM operation.

Related: Cube
More information: Iris Data Structures

Coordinate Factory#

A coordinate factory derives coordinates (sometimes referred to as derived coordinates) from the values of existing coordinates. E.g. A hybrid height factory might use “height above sea level” and “height at ground level” coordinate data to calculate a “height above ground level” coordinate.

Related: Cube
More information: Iris Data Structures

Dask#

A data analytics python library. Iris predominantly uses Dask Arrays; a collection of NumPy-esque arrays. The data is operated in batches, so that not all data is in RAM at once.

Related: Lazy Data | NumPy
More information: Real and Lazy Data

Fields File (FF) Format#

A meteorological file format, the output of the Unified Model.

GRIB Format#

A WMO-standard meteorological file format.

Lazy Data#

Data stored in hard drive, and then temporarily loaded into RAM in batches when needed. Allows of less memory usage and faster performance, thanks to parallel processing.

Related: Dask | Real Data
More information: Real and Lazy Data

Long Name#

A name describing a phenomenon, not limited to the the same restraints as standard name.

Related: Standard Name | Cube
More information: Iris Data Structures

Matplotlib#

A python package for plotting and projecting data in a wide variety of formats.

Related: CartoPy | NumPy
More information: matplotlib

Metadata#

The information which describes a phenomenon. Within Iris specifically, all information which distinguishes one phenomenon from another, e.g. units or Cell Methods

Related: Phenomenon | Cube
More information: Metadata

NetCDF Format#

A flexible file format for storing multi-dimensional array-like data. When Iris loads this format, it also especially recognises and interprets data encoded according to the CF Conventions.

NumPy#

A mathematical Python library, predominantly based around multi-dimensional arrays.

Related: Dask | Cube | Xarray
More information: NumPy.org

Phenomenon#

The primary data which is measured, usually within a cube, e.g. air temperature.

More information: Iris Data Structures

Post Processing (PP) Format#

A meteorological file format, created from a post processed Fields File (FF) Format.

More information: PP Wikipedia Page

Real Data#

Data that has been loaded into RAM, as opposed to sitting on the hard drive.

Related: Lazy Data | NumPy
More information: Real and Lazy Data

Standard Name#

A name describing a phenomenon, one from a fixed list defined at CF Standard Names.

Related: Long Name | Cube
More information: Iris Data Structures

Unit#

The unit with which the phenomenon is measured e.g. m / sec.

Related: Cube
More information: Iris Data Structures

Xarray#

A python library for sophisticated labelled multi-dimensional operations. Has a broader scope than Iris - it is not focused on meteorological data.

Related: NumPy
More information: Xarray Documentation


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